darfix.core.geneticShiftDetection.GeneticShiftDetection#

class darfix.core.geneticShiftDetection.GeneticShiftDetection(data, optimal_shift)[source]#

Bases: object

Class performing feature selection with a genetic algorithm. Selects the best shift to apply to each image from a set of images. Given a linear (increasing through the stack) shift that produces optimal results, it tries to find the best 2d normal distibution that, added to the optimal shift, produces the best result.

Parameters:

  • data (array_like) – Stack of images.

  • optimal_shift (array_like) – Array with 2 rows (y and x) and len(data) columns with an optimal linear shift.

crossover(parents)[source]#

Given a set of parents individuals, it randomly mixes pairs of them to create a new generation of children. A fixed number of parents, elite_size, automatically becomes a child. It assumes that a first portion, bigger than elite_size, of parents is from the elite choosen in the select() method.

Parameters:

parents (array_like) – Individuals previously chosen to become parents.

fit(mean, sigma, n_gens, size)[source]#

Computes the genetic algorithm.

Parameters:

  • mean (Union(list, tuple)) – 2d vector to be the mean of the starting population of normals.

  • sigma (number) – Standard deviation used to create the covariance matrix (set in the diagonal of 2x2 matrix).

  • n_gens (int) – number of generations to compute.

  • size (int) – number of individuals of the population.

Returns:

The genetic algorithm

Return type:

GA

fitness(population, shift_approach='linear')[source]#

Finds the score of each of the individuals of a population, by means of the fitness function.

Parameters:

  • population (array_like) – List of individuals to score.

  • shift_approach (str) – Name of the shift approach to be used.

Returns:

ndarray, ndarray

generate(population)[source]#

Creates a new generation of indidivuals.

Parameters:

population (array_like) – Actual population of individuals.

initialize(size)[source]#

Initializes size normal distributions to be used as initial populations.

Parameters:

size (int) – Size of the initial population.

Returns:

ndarray

mutate(children)[source]#

Given a set of children individuals, it randomly mutates some of their gens.

Parameters:

children (array_like) – List of individuals.

select(population, scores)[source]#

Selects the parents to breed the new generation. A fixed number self.elite_size of best score population automatically becomes a parent, the others are added with a certain probability, which increases as the fitness score.

Parameters:

  • population (array_like) – Population ordered by higher score.

  • score (array_like) – Score, ordered from top to bottom, of each inidivual.

property support_#

Returns the best chromosome from the last iteration.